AI 中文总结
该研究提出嵌入PINN的热力耦合框架，结合守恒律与多信号特征，实现纳秒激光烧蚀下钢材多种力学性能的高精度非接触反演，性能显著优于基线方法。
AI 中文摘要
准确评估时效或服役条件下钢材的力学性能仍是一项重大挑战。我们提出一种基于能量守恒的纳秒激光烧热力-力耦合框架，将其嵌入物理信息神经网络（PINN）以实现多种力学性能的同时反演。定义热力耦合系数，用于统一描述激光辐照下不同变形阶段输入激光能量在热扩散、机械功和等离子体屏蔽之间的动态分配。此外，耦合方程中难以测量的物理特征，被替换为通过同步采集光谱、冲击波和表面波信号获得的可实验获取特征。利用210个实验数据集，该框架同时以高精度反演得到杨氏模量、屈服强度、极限抗拉强度和显微维氏硬度，对应的R²分别为0.9927、0.9912、0.9916和0.9959，显著优于基线方法（用于杨氏模量的超声波速回归，R²=0.0012）。与线性归一化和无约束神经网络的比较表明，PINN通过嵌入守恒律约束实现了接近1的精度。偏依赖分析进一步揭示了输入特征与力学性能之间的非线性耦合规律。所提出的范式整合守恒律、可测量特征和物理信息学习，为纳秒激光烧蚀条件下多种材料性能的非接触、高精度、物理一致的多对多反演提供了通用方法。
英文摘要
Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input laser energy among thermal diffusion, mechanical work and plasma shielding across different deformation stages under laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave and surface-wave signals. Using 210 experimental datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength and micro-Vickers hardness with high accuracy (R2=0.9927, 0.9912, 0.9916 and 0.9959 respectively), significantly outperforming the baseline method (ultrasonic velocity regression for E, R2=0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features and physics-informed learning, offers a universal approach for non-contact, high-precision and physically consistent multi-to-multi inversion of multiple material properties under nanosecond laser ablation conditions.
CommentsPublished in Applied Physics Letters. This is the authors' version. 6 pages, 5 figures
Journal refY. Zhou, J. Wu, Z.Y. Song, J.H. Li, X.Y. Guo, H. Sun, Y.H. Hang, C.X. Pei, and X.W. Li, Appl. Phys. Lett. 127 (24), 244101 (2025)
